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I Can't Think With All This Noise: Inferring Strategies Using Symbolic Regression

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  • Jim Warnick

Abstract

We use symbolic regression (implemented by a genetic program) to analyze the role of agent expectation formation in games. In the model, agents attempt to infer the strategies of opponents through regression and then best respond using this information. Though agents use deterministic strategies, behavior that resembles a mixed strategy emerges. When one agent uses more complicated strategy primitives (building blocks) significant performance advantages are realized. However, even small amounts of noise in the system can eliminate this advantage. By changing the design of the game we show that it is crucial to accurately infer past actions in order to realize performance advantages from complexity.

Suggested Citation

  • Jim Warnick, 1999. "I Can't Think With All This Noise: Inferring Strategies Using Symbolic Regression," Working Papers 99-08-057, Santa Fe Institute.
    • Handle: RePEc:wop:safiwp:99-08-057
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    References listed on IDEAS

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    1. Abreu, Dilip & Rubinstein, Ariel, 1988. "The Structure of Nash Equilibrium in Repeated Games with Finite Automata," Econometrica, Econometric Society, vol. 56(6), pages 1259-1281, November.
    2. John Duffy & Jim Warnick, 1999. "Using Symbolic Regression to Infer Strategies from Experimental Data," Computing in Economics and Finance 1999 1033, Society for Computational Economics.
    3. Miller, John H., 1996. "The coevolution of automata in the repeated Prisoner's Dilemma," Journal of Economic Behavior & Organization, Elsevier, vol. 29(1), pages 87-112, January.
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    Keywords

    Genetic program; symbolic regression;

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